The Trading Behavior on Ex-Dividend Day: A Study on French Stock Market

Transcription

1 DOI: /IPEDR V69. 8 The Trading Behavior on Ex-Dividend Day: A Study on French Stock Market Hung T. Nguyen 1, Hang V. D. Pham 2, and Hung Nguyen 3 1, 3 College of Business, Massey University, New Zealand 2 Sobey School of Business, Saint Mary s University, Canada Abstract. This paper studies trading behavior around ex-dividend days on French stock market in the sample period from January 1, 2012 to December 31, We find that on average, abnormal turnover significantly decreases before the ex-dividend day; whereas, abnormal return follows a volatile trend around event dates. We record a remarkable drop of abnormal turnover (from 7.1% to -3.2%) and slight reduction of abnormal return (from 0.89% to -0.01%) on the first day after the event date. In addition, we investigate the effect of tax heterogeneity to trading volume and find strong evidences to reject the increase in trading volume when tax heterogeneity among investors exits. Keywords: Ex-Dividend Day, Abnormal Turnover, Trading Behavior. 1. Introduction For years, trading behavior around ex-dividend days remains a controversial issue for both academics and practitioners. Financial theory provides ambiguous forecasts about stock price and trading volume around ex-dividend day. Several studies point out that, on ex-dividend days when stock is traded without dividend, on average, stock price drops by less than the value of dividend due to the effect of income tax (Michaely and Vila, 1996; Murray Frank and Ravi Jagannathan, 1988; Booth and Johnston, 1984; Rakesh Bali and Gailen L. Hite, 1998). From different approaches, a number of researches document a mixed result of trading volume around ex-dividend days: a decrease of trading volume before scheduled announcement and an increase before unscheduled announcement (Joon Chae, 2005; Fabiano, 2008). Currently, several studies examine trading behaviors around ex-dividend days by considering the effect of the tax heterogeneity and transaction cost (Michaely and Vila, 1995; Michaely and Murgia, 1995). However, the puzzle is not solved yet and investor demands more empirical researches to unmask the stock behaviors around exdividend day. In this paper, we study the trading behavior around ex-dividend day on French stock market by applying the method suggested by Joon Chae (2005) with the main interest on abnormal turnover and abnormal return. Our objective is to seek a persuasive answer for the question of how stock returns and trading volumes performing around ex-dividend days. In addition, the relationship between information asymmetry and trading behavior around ex-dividend date is of our most concern. Furthermore, we want to examine how tax heterogeneity theory affects trading behavior around ex-dividend day on French stock market. As previous studies employ either abnormal turnover or abnormal return to investigate the stock behaviors around exdays, our research will contribute to existing literatures by suggesting a new measure of trading behavior that takes into account both abnormal turnover and abnormal return. We provide more insights on current findings about the stock behavior in French stock market with an empirical approach. Finally, we address the problems of previous studies when dealing with time-series data by conducting robustness tests. Corresponding author. Tel.: address: nguyenthehung@neu.edu.vn. 45

2 With such objectives, we test a sample period from January 1, 2012 to December 31, 2012 in daily basic with data extracted from Datastream. Following Joon Chae (2005), we conduct an event study over 949 stocks existing in French stock market. In this study, two separate methods, a market model and a constant mean model, are employed to calculate abnormal return and abnormal turnover. The estimation window (nest) is 200 trading days and the event window is 5 days before and after event date (nwindow=10). We find that abnormal return changes by 21.25% from t=-1 to t=0 with highly significant t-stat of 8.3. However, a remarkable drop of abnormal turnover (from 7.1% to -3.2%) and slight reduction of abnormal return (from 0.89% to -0.01%) from event day to day t=1 with t-stat of and respectively, is recorded. In general, the study finds a significant decrease in the level of abnormal turnover in the period before the exdividend day. We address the robustness of these findings in several approaches. We first consider larger estimation window (nest=250) and keep event window unchanged (nwindow=10). We then estimate the abnormal return and abnormal turnover by using larger event window (nwindow=20) and keep estimation window constant (nest=200). Finally, we consider median abnormal turnover and return for comprehensively analyzing the trading behaviors around ex-dividend days. For further analyzing investor s behavior and to answer the question why trading volume decrease prior to the ex-dividend date, we run the regression of abnormal trading volume and turnover on information asymmetry proxies and control variables to see whether information asymmetry actually reduce the motivation of trading. The dependent variable is defined as the cumulative abnormal trading volume over the period of t=-10 and t=-2. We find that size does positively correlate with trading volume, but that alone is not sufficient to fully explain abnormal turnover. In addition, there is almost no relationship between market-to-book ratio and trading volume as well as abnormal return. Further, we use dummy variables to take into account the nature of industries and find that ex-dividend event has a relatively low impact to raw material industry. The rest of the paper is structured as follow. Section II discusses the literature review. Section III reports the methodology and data description. Section IV discusses the results of empirical analysis by providing summary statistic and plot of cumulative abnormal return and turnover. Section V examines robustness of the findings in several approaches and reports the regression analysis. Section VI provides concluding remarks and reports research limitations. 2. Literature Review Trading behavior has been a subject of intensive studies from the very beginning of financial market. Campell and Beranek (1995), by examining stocks listed on New York Stock Exchange (NYSE), conclude that on average, the stock price drops about 90 percent of the amount of the dividend (Campell and Beranek, 1995). Michealy and Vila (1995), however, argue that even in the market without transaction cost, the price drop on ex-dividend day need not to be equal with dividend amount after studying the same market. Michealy and Vila (1995) explain the higher market trading volume around ex-day is a function of tax heterogeneity among traders. Particularly, traders with different tax rates of dividend and capital gain will have motivation to trade with each other around the ex-dividend day, thus stimulate trading volume (Michealy and Vila (1995). This argument is also supported by a study of Koski and Scruggs (1998) that documents some abnormal trading volume consistent with corporate dividend-capture trading. In this paper, we apply the previous findings and suggest a new measure to investigate the stock behavior around ex-dividend day in French stock market where tax difference between dividend (40%) and capital gain (34%) is noticeable. If tax heterogeneity thesis from Michaely and Vila (1995) can explain French stock market behavior among ex-dividend date, we do expect an increase in trading volume around event date 46

3 since there is a tax advantage between dividend income (40%) and capital gain income (34%), different investors are motivated to trade with each other s (Michealy and Vila, 1995). We examine the following hypotheses: Hypothesis 1: Around the ex-dividend day, trading volume should increase only if there is effect of tax heterogeneity among investors. On the other hand, Michealy and Murgia (1995) examine stocks in Milan stock market and conclude that the ex-dividend day price declines and abnormal volume increase in relation to the event date cannot be explained by the relative after-tax valuation of dividends and capital gains alone. As shown in Black (1986) and Wang (1994), uninformed investors will trade less in the financial market if there is a high chance of dealing with informed counterparty. It is reasonable to infer that trading volume will decrease prior to the announcement date. A necessary condition for this prediction to hold is that uninformed investor must recognize a high level of information asymmetry and attempts to trade by the informed trader. Therefore, only before scheduled announcement, such as ex-dividend day, can uninformed investor realize their weakness and avoid unnecessary trading. In response, total trading volume before ex-dividend day should decrease. The decision of giving up trading depends heavily on the how much uninformed investor forecast about information asymmetry. In other word, the trading volume before ex-dividend day should be inversely correlated with the level of information asymmetry. We, therefore, come up with the decision on testing the correlation between trading volume and commonly used proxies for information asymmetry, including company size, market-to-book value and industry dummies. Hypothesis 2: Trading volume before ex-dividend day is negatively correlated with level of ex ante information asymmetry. 3. Methodology We test a sample period from January 1, 2012 to December 31, 2012 with data extracted from Datastream, including daily stock return, daily local market index, daily trading volume, daily number of outstanding share and ex-dividend days. Following the method suggested by Joon Chae (2005), we conduct an event study over 949 stocks existing in French stock market. First of all, we used return index (RI) and market index (LI) to calculate individual stock return index and market return. We then apply the market model to calculate abnormal returns. When it comes to abnormal turnover, we apply the similar procedure with two adjustments. First, for data inputs, we use trading volume (VO), number of share outstanding (NOSH) to calculate market turnover. Following Joon Chae (2005), I chose to apply logarithms for turnover to reduce outliers close to normal distribution. Second, to estimate abnormal turnover, we apply a constant mean model instead of market model as it often yields similar results to more complicated model while the drop in variance of abnormal turnover is negligible (Stephen J. Brown and Jerold B. Warner, 1985). Since the abnormal returns and abnormal turnovers for investigated period are calculated, we compute three indicators including average abnormal return (AAR), cumulative abnormal return (CAR) and cumulative average abnormal return (CAAR) and test these indicators in investigated period. The corresponding indicators regarding turnovers are AAT, CAT and CAAT. For each event date, we examine the four following periods: (-10,-2), (-1,1), (-1,0) and (2,10). The following section will provide the results of estimated parameters and further approaches to test trading behavior around event dates, including random day abnormal return and turnover, robustness test and regression analysis. 4. Empirical Analysis 4.1. Summary statistic 47

4 Table I reports summary statistics of daily return and turnover from stock in French stock market for 1 year (250 trading days). The summary statistics are the averages of estimates for each firm, including mean, standard deviation, skewness, and kurtosis. We calculate the daily turnover by dividing daily trading volume over corresponding outstanding shares. Table I: Summary Statistic Period Mean SD Skewness Kurtosis No. of Firms Daily Return (%) Daily Turnover (%) Log Daily Turnover We reports summary statistic on daily return, turnover and log turnover over 949 stocks in French market within one year from Jan to Dec 31, The literature of trading activity measure on financial market, as summarized by Lo and Wang (2000), is vast and extensive. Previous studies employ a number of methods to measure trading behaviors, including the aggregate turnover (Campell, Grossman, and Wang 1993; LeBaron, 1992), an individual share volume (Andersen, 1996), the number of trading days per year (James and Edmister, 1983) and total number of trade (Conrad, Hameed, and Niden, 1994). In this paper, we use log turnover instead of raw turnover (trading volume divided by outstanding shares) to reduce the risk of fat tail and extreme positive skewness. The log turnover also helps reduce the outliners and thus, the results and findings are more reliable. When it comes to stock return, the market model is employed to calculate stocks return: The cross-sectional skewness and kurtosis of daily return ( and , respectively) is relatively smaller than those derived from trading volume turnover ( and , respectively). We apply the logarithm function of turnover proposed by Ajinkya and Jain (1989) to reduce problem of fat tails and other possible biases of non-normal distribution. As a results, the sknewness and kurtosis of volume turnover decrease to and respectively, much closer to normal distribution. In this paper unless further notice noted, any reference to trading volume, volume or turnover will refer to log turnover as defined in this equation: ( ) ( ) (1) (2) where ( ) 48

5 We apply the constant mean model suggested by Brown and Warner (1985) in equation (2) to calculate abnormal turnover which is slightly different to the approach suggested by Joon Chae (2005). We run the regression of logarithm turnover of estimation period to achieve the coefficients. Measuring trading volume near announcement by this method would provide more accurate prediction on expected abnormal turnover rather than taking the difference between log turnover during the test period and the estimation period Daily abnormal return and turnover around ex-dividend date Table II reports the daily abnormal return and turnover around ex-dividend date from existing stocks in French financial market between Jan 1, 2012 and December 3, The abnormal turnover is reported as the difference between log turnover and constant model coefficient from t=-200 to t=-11, where turnover is trading volume divided by shares outstanding. The t-statistics are given to the right of their corresponding figures. Average ( ) is the average abnormal return and turnover from. Table II: Daily Abnormal Return and Turnover around Ex-Dividend Date No. of Obs. Abnormal Return Abnormal Turnover AAR t_aar AAT t_aat Average (-10, -2) Average (2, 10) Average (-1,0) Average (-1, 1) To test the hypotheses, we use the variables described in the previous section to report cross-sectional mean of abnormal return and turnover over different time-series around ex-dividend day, from (-10,-2) and (2,10) to 1 day before and after ex-day performance to capture abnormalities in both return and trading volume. One noticeable point is that abnormal return changes by 21.25% from t=-1 to t=0 with highly significant t-stat of 8.3. However, we record a remarkable drop of abnormal turnover (from 7.1% to -3.2%) and slight fall of abnormal return (from 0.89% to -0.01%) from event day to day t=1 with t-stat of and respectively. On average, there is a significant decrease in the level of abnormal turnover prior to the ex-dividend day. The negative 8.5% mean abnormal turnover in the period of t=-10 to t=-2 and negative 10.7% in the period of t=2 to t=10 reject the hypothesis 1 of tax heterogeneity among investors. Following t=0, the 49

6 trading volume witnesses even a lager fall of average 10.7% from day 2 to day 10, and the stock return decrease minor amount as Ill (-0.04%). This is probably due to the existence of short-term traders according to the hypothesis proposed by Kalay (1982). Particularly, Kalay (1982) suggest that an investor would try to buy stock before the ex-dividend date and sell it on the ex-day if the stock drops less than the dividend payout. Karpoff and Walkling (1988) document similar findings in NYSE. Moreover, significant t-stat around -2.9 means that the result is statistically significantly at 5%. In addition to results in Table III, we provide four plotted graphs that summarize the cumulative abnormal return (CAAR) and the cumulative abnormal turnover (CAAT) from day -10 to day 10 in Figure 1. These plots show the cumulative return and turnover from t=-10 to +10 that excess over the benchmark. The benchmark coefficient is determined from t=-200 to t=-11 days. For abnormal return, there is a significant difference prior to and after the ex-dividend day. Between t=-10 and t=-1, the CAARs hover around 0% before quickly elevating from 0.04% to 0.89% before the event date. Subsequently after the event date, the cumulative return follows a downward trend before suffering from a loss of % at day 10. In contrast to CAAR, the cumulative abnormal turnover, as expected, appears to be affected by the taxation heterogeneity and short-traders when it decreases for 5 consecutive days. It then recovers at the announcement day before dramatically decreasing from 7% to -15%. To test for the precision of estimated abnormal return and turnover, we randomly choose event dates and event stock and repeat the exact same process as for ex-dividend day. We record very near-zero abnormal return and turnover over the benchmark. These flat lines confirm that the measurement is unbiased and reliable EventWindow CAAR_random CAAT_random CAAR CAAT EventWindow EventWindow EventWindow Fig. 1: Cumulative abnormal return and turnover form t=-10 to t=10. The benchmark abnormal return and coefficient turnover are derived from t=-200 to t=-11, where turnover is daily volume divided by shares outstanding. These plots show cumulative abnormal return (CAAR) and cumulative abnormal turnover (CAAT) from t=-10 to t=+10, that is the cumulative excess over the benchmark. In the two last plots, we select random days as t=0 and the results illustrate that the measure of CAAR and CAAT is unbiased. 5. Robustness In this section, we address the robustness of these findings in several approaches. We first consider larger estimation Window (nest=250) and keep event window unchanged (nwindow=10). We then estimate the abnormal return and abnormal turnover by using larger event window (nwindow=20). Finally, we consider median abnormal turnover and return for comprehensively analyzing the trading behaviors around ex-dividend days. In addition, we conduct a regression analysis for further analyzing trading behaviors around ex-dividend day. 50

7 The following table reports alternative measures of abnormal log turnover. Panel A1 employs larger estimation window of 250 days, Panel A2 tests larger even window of 20 days and Panel B uses the difference between raw and median turnover. The row of (-10,-2) and (-20,-2) report the summary measure of average daily abnormal trading volume in 9 days and 19 days respectively. Table III (Panel A.1): Using Larger Estimation Window (nest=250; nwindow=10) Abnormal Return Abnormal Turnover No. of Obs. AAR t_aar AAT t_aat (-10,-2) (-1,0) (-1,1) (2,10) Table III (Panel A.2): Using Larger Event Window (nest=200; nwindow=20) Abnormal Return Abnormal Turnover No. of Obs. AAR t_aar AAT t_aat (-20,-2) (-1,0) (-1,1) (2,20) Table IV: Panel B: Using Median Abnormal Turnover and Return Abnormal Return Abnormal Turnover No. of Obs. AAR t_aar AAT t_aat (-10,-2) (-1,0) (-1,1) (2,10) Firstly, we increase the estimation window is from nest=200 days to nest=250 days and report the results in Panel A of Table IV. The expanded evidence is much convincing than the figure in Table III. Particularly, the aggregate abnormal turnover decrease more than 63% over 9 days compares to 8.5% in Table III. The t- stats are highly significant around the ex-dividend day (-5.8 and respectively). On the other hand, the effect of increased estimate window on abnormal return is slightly stronger than figures in Table III. The AAR (2,10) is negative of -0.35% instead of -0.04%. In Panel A.2, we extend the testing period for 10 days more, turning the window to (-20,+20). The trends recorded are quite similar to results which we have reported so far. The average trading volume in the period of (-20,2) is 263% lower than the benchmark with significant t-start of -16.7; whereas, the slope of abnormal return 1 day prior to the event day is less steeper. In brief, in either large estimate window or large event window, the results remain consistent. That is, there is always a downward trend of abnormal return after the ex-dividend day regarding the existence of dividend payout. Due to the limitation of this research, we do not focus on the reason of this phenomenon. Whether the return amount decline in French stock market is positively correlated with the corresponding dividend yield (Campbell and Beranek, 1955) or it is not (Michael and Vila, 1995)?. That is an open question left for further researches. 51

8 In the second part of robustness, we examine the median of stock return in estimation period instead of average market return to calculate abnormal return, and median of constant mean model instead of coefficient in equation (2). In panel B, we use median raw turnover between t=-200 and t=-11 as the benchmark. Once again, we observe the decrease of aggregate abnormal turnover 9 days around the event date. There are slight upward trend of abnormal return from t=-10 to t=1, and then a downward slope as similar as previous results. Intercept Log Market Cap Table V: Regression Analysis Panel A: Abnormal Return Market-tobook Financial value service Oil & Mining Volatility R² 0.871** (2.26) (0.17) *** (-1.57) (0.30) (5.99) 0.923*** (4.74) (-0.77) * *** (-1.94) (-0.99) (6.05) 0.749*** 3.476*** (3.78) (3.59) (0.96) ** 3.124*** *** (-2.247) (3.33) (0.92) (5.84) *, **, *** indicate significance at 0.10, 0.05 and 0.01 level, respectively. Intercept Log Market Cap Marketto-book value Panel B: Abnormal Turnover Financial service Oil & Mining Absolute Return [-10,- 2] (1.07) (0.25) *** (-1.17) (-0.12) (4.23) (1.616) (0.07) ** *** (-2.01) (-0.18) (4.38) 0.749*** 3.476*** (3.78) (3.59) (0.96) *** *** (-1.69) (1.28) (2.95) (4.57) *, **, *** indicate significance at 0.10, 0.05 and 0.01 level, respectively. Table V reports the results of regressing abnormal log turnover before the ex-dividend date on proxies of ex ante information asymmetry. The coefficients are the time series averages of the coefficients from cross-sectional regressions. The t-statistics are given below the corresponding coefficient in parentheses. The dependent variable is defined as the difference between average log turnover from t=-10 to t=-2 and intercept coefficient from constant model of log turnover from t= -200 to t= -11. means the average of the adjusted R-squares in each cross-sectional regression. Several previous studies conclude that greater information asymmetry leads to less trading ((Michaely and Vila, 1995; Michaely and Murgia, 1995). For further analyzing investor behavior and to answer the question why trading volume decrease prior to the ex-dividend date, we run the regression of abnormal trading volume on information asymmetry proxies and control variable to see whether information 52 R²

9 asymmetry actually reduce the motivation of trading. The dependent variables are defined as the cumulative abnormal trading volume over the period of t=-10 and t=-2. We use Fama and Macbeth (1973) type regression: The notation is the average daily abnormal log turnover between t=-10 and t=-2 at quarter q for company i; is a proxy for information asymmetry at quarter q for company i, including firm size, market-to-book value and industry dummies; and is a control variable for risk factor. For robust testing, we apply logarithm of market capitalization to bring down any outliners close to normally distributed. The market-to-book ratio equals the ratio of market value of assets to book value of assets. Industry dummies are financial service and oil & mining specification. Any firm that in financial service industry is defined as 1; otherwise they are defined as 0. Similarly, firms in oil & mining sector are defined as 1 and otherwise 0. For abnormal return, we choose return volatility as a proxy for risk factor since previous empirical studies prove that volatility increase around ex-dividend day (Donders and Vorst, 1996; Joon Chae, 2005). For abnormal turnover, we choose the absolute value of cumulate abnormal return CAR [-10,-2] as a proxy for control variable. All of proxies are widely used in financial analyzing and proved to have intuitive economic relation with information asymmetry by finance literature (Joon chae, 2005). Atiase (1985) study the relationship of firm size and private pre-disclosure information availability. The empirical evidence shows that larger firms are more transparent, and have less information asymmetry before scheduled announcement. Hypothesis 2 states that the larger ex ante information asymmetry, the less uninformed investors are willing to trade. We, therefore, should expect a positive correlation between firm size and trading volume prior to ex-dividend date. The market-to-book ratio is significantly positively related to the proportion of a firm s debt that is privately placed (Krishnaswami, Spindt, and Subramaniam, 1999). Given that most of outsiders cannot have access to firm s private information, a larger market-to-book ratio implies greater information asymmetry. Hence, we should observe an inverse relation between the market-to-book ratio and the trading volume. Because different nature of each companies business, ex-dividend date in some industries release more meaningful information than others. For instance, the performances of oil & mining firms rely heavily on the market price of raw crude, which all traders can obtain from publicly sources (Joon Chae, 2005). As a consequence, a low dividend payout is expected when oil & mining firms witness a rough year of continuously fluctuating raw ingredient s price. We should observe a positive coefficient for the dummy variables of specific industries such as oil and mining, financial services and etc. Table V reports the results of regressing abnormal log turnover before the ex-dividend date on proxies of ex ante information asymmetry. The coefficients are the time series averages of the coefficients from crosssectional regressions. t-statistics are calculated with the standard errors of these time weighted series. The dependent variable is defined as the difference between average log turnover from t=-10 to t=-2 and intercept coefficient from constant model of log turnover from t=-200 to t=-11. As shown in Table V, the coefficient of size factor has shown the same negative side as of trading volume (-0.511) with insignificant t-stat of , which mean size does positively correlated with trading volume, but it is not fully able to explain abnormal turnover. Meanwhile, the coefficient of market-to-book ratio, supposed to be positive, is slightly negative ( ). Since corresponding t-statistics are not significant even with the existence of control variable, we cannot conclude any relationship between this indicator and trading volume as well as abnormal return. As expected, the coefficient of raw material industries, such as oil and mining, in abnormal turnover regression with control variable is with significant t-stat of Apparently, uninformed investors do not worry much about their information disadvantage thanks to the availability of oil & crude pricing information. Ex-dividend event has a relatively low impact on stock price of firms in these industries. On the other hand, when it comes to financial services industry, the coefficients are significantly positive in abnormal result regression but turn into negative in turnover section. Since the t-statistic for turnover section is not significant in either 5% or 10%, we do not have the strong evidence for the effect of asymmetry information on price change in financial sector around ex-dividend day. 6. Conclusion 53 (3)

10 In this paper, we study the trading behavior around ex-dividend day on French stock market by applying the method suggested by Joon Chae (2005) with the main interest on abnormal turnover and return. Our research contributes to existing literatures by suggesting a new measure of trading behavior that takes into account both abnormal turnover and abnormal return. We test a sample period from January 1, 2012 to December 31, 2012 with data extracted from Datastream. Following Joon Chae (2005), we calculate abnormal return and abnormal turnover by employing two separate methods: a market model and a constant mean model. We find that abnormal return changes by 21.25% from t=-1 to t=0 with highly significant t-stat of 8.3. We, however, record a remarkable drop of abnormal turnover (from 7.1% to -3.2%) and slight reduction of abnormal return (from 0.89% to -0.01%) from event day to day t=1 with t-stat of and respectively. In general, we find a significant decrease in the level of abnormal turnover prior to the ex-dividend day. We address the robustness of these findings in several approaches. The results of robustness testing do support the previous findings. For further analyzing investor behavior, we run the regression of abnormal trading volume and abnormal turnover on information asymmetry proxies and control variables to see whether information asymmetry actually reduces the motivation of trading. We find that size does positively correlated with trading volume, but that alone is not sufficient to fully explain abnormal turnover. In addition, there is no relationship between market-to-book ratio and trading volume as well as abnormal return. Further, we use dummy variables to take into account the nature of industry and find that ex-dividend event has a relatively low impact on stock price of firms in oil and raw material industry. This research has some limitations. First, the investigated period in this paper is only 1 year and the findings may be inconsistent if large sample period is tested. Second, in regression analysis, we do not investigate the impact of bid-and-ask spread and dividend yield to trading behavior around ex-dividend day. Since these two proxies are alternative measures of information asymmetry, we are interested in dig up deeper these proxies in future research. Finally, we overcome the limitations of previous researches when dealing with time-series data by conducting robustness test. To fully correct any biases when doing empirical research with time-series data, we suggest future researches should combine robustness test and other approaches for comprehensively analyzing the trading behavior around ex-dividend days. 7. Acknowledgements We thank Nuttawat Visaltanachoti, Linh Nguyen, and workshop participants at Massey University, New Zealand and National Economics University of Vietnam for their generous comments. We thank Nam H. Vu, Huong D. Vu, Kien T. Tran and Lan Anh Nguyen for their research assistance. 8. Reference [1] T. G. Andersen, Return volatility and trading volume: An information flow interpretation of stochastic volatility, The Journal of Finance, vol. 51, no. 1, pp [2] B. B. Ajinkya and P. C. Jain, The behavior of daily stock market trading volume, Journal of Accounting and Economics, vol. 11, no. 4, pp , [3] R. K. Atiase, Predisclosure information, firm capitalization, and security price behavior around earnings announcements, Journal of Accounting Research, vol. 23, no. 1, pp , [4] F. Black, Noise, The Journal of Finance, vol. 41, no. 3, pp , [5] L. D. Booth and D. J. Johnston, The ex-dividend day behavior of Canadian stock prices: Tax changes and clientele effects, The Journal of Finance, vol. 39, pp , [6] S. J. Brown and J. B. Warner, Using daily stock returns: The case of event studies, Journal of Financial Economics, vol. 14, no. 1, pp. 3-31, [7] W. Brock, J. Lakonishok, and B. LeBaron, Simple technical trading rules and the stochastic properties of stock returns, The Journal of Finance, vol. 47, no. 5, pp , [8] J. A. Campbell and W. Beranek, Stock price behavior on ex dividend dates, The Journal of Finance, vol. 10, no. 4, pp [9] J. Y. Campbell, S. J. Grossman, and J. Wang, Trading volume and serial correlation in stock returns, The Quarterly Journal of Economics, vol. 108, no. 4, pp ,

11 [10] J. Chae, Trading volume, information asymmetry, and timing information, The Journal of Finance, vol. 60, no. 1, pp , [11] J. S. Conrad, A. Hameed, and C. Niden, Volume and autocovariances in short horizon individual security returns, The Journal of Finance, vol. 49, no. 4, pp , [12] Donders, W. M. Monique, and C. F. Vorst Ton, The impact of firm specific news on implied volatilities, Journal of Banking and Finance, vol. 20, pp , [13] Fabiano Jonfilippo. (2008). Asymmetric Information and Firms Trading Volume before a Scheduled Announcement: The Swiss Evidence. [Online]. Available: or [14] E. F. Fama and J. D. MacBeth, Risk, return, and equilibrium: Empirical tests, The Journal of Political Economy, pp , [15] C. James and R. O. Edmister, The relation between common stock returns trading activity and market value, The Journal of Finance, vol. 38, no. 4, pp , [16] J. L. Koski and J. T. Scruggs, Who trades around the ex-dividend day? Evidence from NYSE audit file data, Financial Management, pp , [17] A. Kalay, The ex dividend day behavior of stock prices: a re examination of the clientele effect, The Journal of Finance, vol. 37, no. 4, pp , [18] J. M. Karpoff and R. A. Walkling, Short-term trading around ex-dividend days: Additional evidence, Journal of Financial Economics, vol. 21, no. 2, pp , [19] R. Michaely and M. Murgia, The effect of tax heterogeneity on prices and volume around the ex-dividend day: Evidence from the Milan stock exchange, Review of Financial Studies, vol. 8, no. 2, pp , [20] R. Michaely and J. L. Vila, Investors' heterogeneity, prices, and volume around the ex-dividend day, Journal of Financial and Quantitative Analysis, vol. 30, no. 2, pp , [21] R. Michaely and J. L. Vila, Trading volume with private valuation: Evidence from the ex-dividend day, Review of Financial Studies, vol. 9, no. 2, pp , [22] Murray Frank and Ravi Jagannathan. (1988). Why do stock prices drop by less than the value of the dividend? Evidence from a country without taxes. Journal of Financial Economics. 47(2). pp [Online]. Available: [23] J. Wang, A model of competitive stock trading volume, Journal of political Economy, pp , [24] Rakesh Bali and L. Gailen Hite. (1998). Ex dividend day stock price behavior: discreteness or tax-induced clienteles? Journal of Financial Economics. 47 (2). pp [Online]. Available: [25] S. J. Brown and J. B. Warner. (1985). Using daily stock returns: The case of event studies. Journal of Financial Economics. 14(1). pp [Online]. Available: [26] R. X. Michaely and J. L.Vila, Investors' heterogeneity, prices, and volume around the ex-dividend day, Journal of Financial and Quantitative Analysis, vol. 30, no. 2, pp , [27] S. Krishnaswami, P. A. Spindt, and V. Subramaniam, Information asymmetry, monitoring, and the placement structure of corporate debt, Journal of Financial Economics, vol. 51, no. 3, pp ,